DATA GOVERNANCE, INTEROPERABILITY AND STANDARDIZATION: ORGANIZATIONAL ADAPTATION TO PRIVACY REGULATION

The increasing availability of data can afford dynamic competitive advantages among data-intensive corporations, but governance bottlenecks hinder data-driven value creation and increase regulatory risks. We analyze the role of two technological features of data architecture that facilitate internal data governance – Application Programmatic Interfaces (APIs) that publish interdepartmental data and standardization of identity and access management (IAM) software – in shaping large dataintensive corporations’ adaptation to privacy regulation. Using annual establishment data for the largest U.S. financial services corporations and the enforcement of the General Data Protection Regulation (GDPR) in 2018 as a natural experiment, we show that internal data APIs and standardization of IAM software significantly mitigate establishments’ revenue loss and IT budget reduction in response to GDPR enforcement. Compliance costs measured by IT hiring increased substantially after GDPR enforcement only for firms without internal data APIs. Our findings highlight the importance of interoperability and standardization as technical conditions that facilitate dynamic integrative capability, allowing large data-intensive corporations to ensure proper data governance and adapt to privacy regulation

Innovation and Design in the Age of Artificial Intelligence

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Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle

This study examines the impact of GitHub Copilot on a large sample of Copilot users (n=934,533). The analysis shows that users on average accept nearly 30% of the suggested code, leading to increased productivity. Furthermore, our research demonstrates that the acceptance rate rises over time and is particularly high among less experienced developers, providing them with substantial benefits. Additionally, our estimations indicate that the adoption of generative AI productivity tools could potentially contribute to a $1.5 trillion increase in global GDP by 2030. Moreover, our investigation sheds light on the diverse contributors in the generative AI landscape, including major technology companies, startups, academia, and individual developers. The findings suggest that the driving force behind generative AI software innovation lies within the open-source ecosystem, particularly in the United States. Remarkably, a majority of repositories on GitHub are led by individual developers. As more developers embrace these tools and acquire proficiency in the art of prompting with generative AI, it becomes evident that this novel approach to software development has forged a unique inextricable link between humans and artificial intelligence. This symbiotic relationship has the potential to shape the construction of the world's software for future generations

Josephson effect between superconducting nanograins with discrete energy levels

We investigate the Josephson effect between two coupled superconductors, coupled by the tunneling of pairs of electrons, in the regime that their energy level spacing is comparable to the bulk superconducting gap, but neglecting any charging effects. In this regime, BCS theory is not valid, and the notion of a superconducting order parameter with a well-defined phase is inapplicable. Using the density matrix renormalization group, we calculate the ground state of the two coupled superconductors and extract the Josephson energy. The Josephson energy is found to display a reentrant behavior (decrease followed by increase) as a function of increasing level spacing. For weak Josephson coupling, a tight-binding approximation is introduced, which illustrates the physical mechanism underlying this reentrance in a transparent way. The DMRG method is also applied to two strongly coupled superconductors and allows a detailed examination of the limits of validity of the tight-binding model

Strategic Alliances for Technology Adoption: Alliances and Partnerships for Blockchain Adoption

This paper aims to study the relevance and importance of strategic alliances for emerging technology adoption. The case researched and discussed here is Blockchain adoption in the semiconductor industry. As a technology, Blockchain has been around for over a decade and is known to provide tremendous value in business transactions. However, the adoption has not gained traction mainly due to the fact that it takes a network to adopt an industrial Blockchain and cannot work in silos. Most companies are shying away from it as they haven\u27t explored what makes a successful strategy for adoption. A literature review was done on the similar technology adoption in the past. The nature of Blockchain and its network dependency were considered. It was clear that strategic alliances are the way to move forward. The various aspects to be considered while forming an alliance, such as understanding the core competencies, finding the right partners, and form of alliances was studied. The research converged in understanding the fact that companies should move away from the transactional business model and have the right expectations and scoping on all sides, standardization, digital integration, and a steady focus on security and privacy

ON THE PERFORMANCE OF NONPARAMETRIC SPECIFICATION TESTS IN REGRESSION MODELS

Some recently developed nonparametric specification tests for regression models are described in a unified way. The common characteristic of these tests is that they are consistent against any alternative hypothesis. The performance of the test statistics is compared by means of Monte Carlo simulations, analysing how heteroskedasticity, number of regressors and bandwidth selection influence the results. The statistics which do not use a bandwidth perform slightly better if the regression model has only one regressor; otherwise, some of the statistics which use a bandwidth behave better if the bandwidth is chosen adequately. These statistics are applied to test the specification of three commonly used Mincer-type wage equations with Uruguayan and Spanish data; all of them are rejected.

Responsible chain management: a capability assessment framework

In recent years, increased attention has been paid to issues of responsibility across the entire product lifecycle. Responsible behaviour of organizations in the product chain is dependent on the actions of other parties such as suppliers and customers. Only through co-operation and close interaction between the different parties involved is it possible to come to a specified form of responsible chain management. Drawing on stakeholder theory and literature on the resource-based view of the firm, this article presents a framework for assessing the organizational capabilities of responding to claims from internal and external parties. Interpretations of stakeholder interests, integration into business processes, monitoring these processes, and communication with stakeholders are the central processes in this framework. The application of this framework to three cases of responsible chain management illustrates the functioning of the framework as a tool for assessing organizational capabilities

An Experimentally Realizable Weiss Model for Disorder-Free Glassiness

We summarize recent work on a frustrated periodic long-range Josephson array in a parameter regime where its dynamical behavior is identical to that of the $p=4$ disordered spherical model. We also discuss the physical requirements imposed by the theory on the experimental realization of this superconducting network.Comment: 6 pages, LaTeX, 2 Postscript figure